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update week 36

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This commit is contained in:
Morten Hjorth-Jensen
2025-09-03 13:40:18 +02:00
parent 1a1618aa74
commit abd1d73a8f
15 changed files with 668 additions and 119 deletions
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doc/Programs/Regression/ooreg.py
+17 -17
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@@ -30,8 +30,8 @@ class LinearRegression:
return X_bias @ self.weights
class RidgeRegression:
def __init__(self, alpha=1.0):
self.alpha = alpha
def __init__(self, theta=1.0):
self.theta = theta
self.weights = None
def fit(self, X, y):
@@ -39,15 +39,15 @@ class RidgeRegression:
n = X_bias.shape[1]
I = np.eye(n)
I[0, 0] = 0
self.weights = np.linalg.inv(X_bias.T @ X_bias + self.alpha * I) @ X_bias.T @ y
self.weights = np.linalg.pinv(X_bias.T @ X_bias + self.theta * I) @ X_bias.T @ y
def predict(self, X):
X_bias = np.c_[np.ones((X.shape[0], 1)), X]
return X_bias @ self.weights
class LassoRegression:
def __init__(self, alpha=1.0, max_iter=1000, tol=1e-4):
self.alpha = alpha
def __init__(self, theta=1.0, max_iter=1000, tol=1e-4):
self.theta = theta
self.max_iter = max_iter
self.tol = tol
self.weights = None
@@ -65,10 +65,10 @@ class LassoRegression:
if j == 0:
self.weights[j] = rho / np.sum(X_bias[:, j] ** 2)
else:
if rho < -self.alpha / 2:
self.weights[j] = (rho + self.alpha / 2) / np.sum(X_bias[:, j] ** 2)
elif rho > self.alpha / 2:
self.weights[j] = (rho - self.alpha / 2) / np.sum(X_bias[:, j] ** 2)
if rho < -self.theta / 2:
self.weights[j] = (rho + self.theta / 2) / np.sum(X_bias[:, j] ** 2)
elif rho > self.theta / 2:
self.weights[j] = (rho - self.theta / 2) / np.sum(X_bias[:, j] ** 2)
else:
self.weights[j] = 0
if np.linalg.norm(self.weights - weights_old, ord=1) < self.tol:
@@ -79,11 +79,11 @@ class LassoRegression:
return X_bias @ self.weights
class KernelRidgeRegression:
def __init__(self, alpha=1.0, gamma=0.1):
self.alpha = alpha
def __init__(self, theta=1.0, gamma=0.1):
self.theta = theta
self.gamma = gamma
self.X_train = None
self.alpha_vec = None
self.theta_vec = None
def _rbf_kernel(self, X1, X2):
dists = np.sum((X1[:, np.newaxis] - X2[np.newaxis, :]) ** 2, axis=2)
@@ -93,11 +93,11 @@ class KernelRidgeRegression:
self.X_train = X
K = self._rbf_kernel(X, X)
n = K.shape[0]
self.alpha_vec = np.linalg.inv(K + self.alpha * np.eye(n)) @ y
self.theta_vec = np.linalg.pinv(K + self.theta * np.eye(n)) @ y
def predict(self, X):
K = self._rbf_kernel(X, self.X_train)
return K @ self.alpha_vec
return K @ self.theta_vec
if __name__ == "__main__":
np.random.seed(42)
@@ -106,9 +106,9 @@ if __name__ == "__main__":
models = {
"linear": LinearRegression(),
"ridge": RidgeRegression(alpha=1.0),
"lasso": LassoRegression(alpha=0.1),
"kernel_ridge": KernelRidgeRegression(alpha=1.0, gamma=5.0)
"ridge": RidgeRegression(theta=1.0),
"lasso": LassoRegression(theta=0.1),
"kernel_ridge": KernelRidgeRegression(theta=1.0, gamma=5.0)
}
for name, model in models.items():